Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI) Article Swipe
Joaquim Carreras
,
Haruka Ikoma
,
Yara Yukie Kikuti
,
Shunsuke Nagase
,
Atsushi Ito
,
Makoto Orita
,
Sakura Tomita
,
Y. Tanigaki
,
Naoya Nakamura
,
Yohei Masugi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/cancers17152428
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/cancers17152428
Background/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging. Methods: This study designed a convolutional neural network based on ResNet architecture to classify a large series of 221 cases, including 177 follicular lymphoma and 44 reactive lymphoid tissue/lymphoid hyperplasia, which were stained with hematoxylin and eosin (H&E). Explainable artificial intelligence (XAI) methods were used for interpretability. Results: The series included 1,004,509 follicular lymphoma and 490,506 reactive lymphoid tissue image-patches at 224 × 244 × 3, and was partitioned into training (70%), validation (10%), and testing (20%) sets. The performance of the training (training and validation sets) had an accuracy of 99.81%. In the testing set, the performance metrics achieved an accuracy of 99.80% at the image-patch level for follicular lymphoma. The other performance parameters were precision (99.8%), recall (99.8%), false positive rate (0.35%), specificity (99.7%), and F1 score (99.9%). Interpretability was analyzed using three methods: grad-CAM, image LIME, and occlusion sensitivity. Additionally, hybrid partitioning was performed to avoid information leakage using a patient-level independent validation set that confirmed high classification performance. Conclusions: Narrow artificial intelligence (AI) can perform differential diagnosis between follicular lymphoma and reactive lymphoma tissue, but it is task-specific and operates within limited constraints. The trained ResNet convolutional neural network (CNN) may be used as transfer learning for larger series of cases and lymphoma diagnoses in the future.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/cancers17152428
- https://www.mdpi.com/2072-6694/17/15/2428/pdf?version=1753194959
- OA Status
- gold
- Cited By
- 1
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412570113
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412570113Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/cancers17152428Digital Object Identifier
- Title
-
Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI)Work title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-22Full publication date if available
- Authors
-
Joaquim Carreras, Haruka Ikoma, Yara Yukie Kikuti, Shunsuke Nagase, Atsushi Ito, Makoto Orita, Sakura Tomita, Y. Tanigaki, Naoya Nakamura, Yohei MasugiList of authors in order
- Landing page
-
https://doi.org/10.3390/cancers17152428Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-6694/17/15/2428/pdf?version=1753194959Direct link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-6694/17/15/2428/pdf?version=1753194959Direct OA link when available
- Concepts
-
Follicular lymphoma, Convolutional neural network, Lymphoma, BCL6, Artificial intelligence, Lymphoid hyperplasia, Computer science, Pathology, Medicine, Pattern recognition (psychology), Germinal center, Immunology, B cell, AntibodyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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74Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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